1. Field of the Invention
The present invention relates to an apparatus and a method for classifying pixels in each frame of a motion picture as foreground or background.
2. Description of the Related Art
In order to monitor moving objects such as people, animals, vehicles and so on, in a moving picture for a surveillance system, a traffic measurement system or a video conference system, it is required to distinguish moving objects from other fixed objects, such as buildings, roads, trees, walls, and various equipment. In other word, it is required to determine whether each pixel in each frame of the moving picture corresponds to a moving object or a fixed object. Hereinafter, moving objects to be distinguished are referred as foreground, and reaming parts are referred as background. Further, a pixel corresponding to foreground is referred as a foreground pixel, and a pixel corresponding to background is referred as a background pixel.
Thanarat Horprasert, et al. “A Statistical Approach for Real-time Robust Background Subtraction and Shadow Detection”, Proc. IEEE, Frame-Rate Application Workshop, Greece, 1999 and Yutaka Satoh, et al. “Robust Background Subtraction based on Bi-polar Radial Reach Correlation”, TENCON 2005, IEEE Region 10, November 2005 disclose a method for determining a pixel type, i.e. foreground or background, based on a reference image, which is a background image or video captured in advance. However, in case background colors are changed due to light or the like while capturing the moving picture, the method is not applicable. Further, in case background colors and foreground colors are similar, it is hard to distinguish them, and accuracy of determination becomes poor.
On the other hand, Ahmed Elgammal, et al. “Background and Foreground Modeling Using Nonparametric Kernel Density Estimation for Visual Surveillance”, Proc. of IEEE, Vol. 90, No. 7, July 2002, discloses a method which generates a statistical model of pixel values from frames of a moving picture, and classifies pixels using the statistical model and a threshold value.
The method uses a fixed threshold value to determine a pixel type of each pixel. Therefore, the result is very sensitive to the threshold value. For example, in case a shadow produced by foreground objects has a color similar to that of a background object, pixels in the shadow area are classified as background with a given threshold value, while they are classified as foreground with a threshold value, which is slightly different from the given threshold value. Thus, a different result arises according to a threshold value to be used.
The problem of the method is that it is sensitive to bright colors and camera characteristics. Therefore, in a region where the background has a high-frequency border, the method is not sufficient for an application, which requires high detection accuracy. When the background has very high-frequency dispersion, the model cannot achieve highly sensitive detection. If the model is generated with small numbers of Gaussian distribution, an accurate result cannot be obtained.